---
     
Hikyuu Quant Framework是一款基于C++/Python的开源量化交易研究框架，用于策略分析及回测（目前主要用于国内A股市场）。其核心思想基于当前成熟的系统化交易方法，将整个系统化交易抽象为由市场环境判断策略、系统有效条件、信号指示器、止损/止盈策略、资金管理策略、盈利目标策略、移滑价差算法七大组件，你可以分别构建这些组件的策略资产库，在实际研究中对它们自由组合来观察系统的有效性、稳定性以及单一种类策略的效果。
👉 **项目地址：**
* [https://github.com/fasiondog/hikyuu](https://github.com/fasiondog/hikyuu)
* [https://gitee.com/fasiondog/hikyuu](https://gitee.com/fasiondog/hikyuu)
* [https://gitcode.com/hikyuu/hikyuu](https://gitcode.com/hikyuu/hikyuu)
👉 **项目首页：**[https://hikyuu.org/](https://hikyuu.org/)
👉 **帮助文档：**[https://hikyuu.readthedocs.io/zh-cn/latest/index.html](https://hikyuu.readthedocs.io/zh-cn/latest/index.html)
👉 **入门示例:** [https://nbviewer.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True](https://nbviewer.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True)
👉 **策略部件库：**[https://gitee.com/fasiondog/hikyuu_hub](https://gitee.com/fasiondog/hikyuu_hub)
👉 感谢网友提供的 Hikyuu Ubuntu虚拟机环境, 百度网盘下载(提取码: ht8j): [https://pan.baidu.com/s/1CAiUWDdgV0c0VhPpe4AgVw?pwd=ht8j](https://pan.baidu.com/s/1CAiUWDdgV0c0VhPpe4AgVw?pwd=ht8j)
示例：
```python
#创建模拟交易账户进行回测，初始资金30万
my_tm = crtTM(init_cash = 300000)
#创建信号指示器（以5日EMA为快线，5日EMA自身的10日EMA作为慢线，快线向上穿越慢线时买入，反之卖出）
my_sg = SG_Flex(EMA(CLOSE(), n=5), slow_n=10)
#固定每次买入1000股
my_mm = MM_FixedCount(1000)
#创建交易系统并运行
sys = SYS_Simple(tm = my_tm, sg = my_sg, mm = my_mm)
sys.run(sm['sz000001'], Query(-150))
```
完整示例参见：[https://nbviewer.jupyter.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True](https://nbviewer.jupyter.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True)
# 为什么选择 Hikyuu？
* **组合灵活，分类构建策略资产库** Hikyuu对系统化交易方法进行了良好的抽象，包含了九大策略组件：市场环境判断策略、系统有效条件、信号指示器、止损/止盈策略、资金管理策略、盈利目标策略、移滑价差算法、交易对象选择策略、资金分配策略。可以在此基础上构建自己的策略库，并进行灵活的组合和测试。在进行策略探索时，可以更加专注于某一方面的策略性能与影响。其主要功能模块如下：
* **性能保障，打造自己的专属应用** 目前项目包含了3个主要组成部分：基于C++的核心库、对C++进行包装的Python库(hikyuu)、基于Python的交互式工具。
* AMD 7950x 实测：A股全市场（1913万日K线）仅加载全部日线计算 20日 MA 并求最后 MA 累积和，首次执行含数据加载 耗时 6秒，数据加载完毕后计算耗时 166 毫秒，详见: [性能实测](https://mp.weixin.qq.com/s?__biz=MzkwMzY1NzYxMA==&mid=2247483768&idx=1&sn=33e40aa9633857fa7b4c7ded51c95ae7&chksm=c093a09df7e4298b3f543121ba01334c0f8bf76e75c643afd6fc53aea1792ebb92de9a32c2be&mpshare=1&scene=23&srcid=05297ByHT6DEv6XAmyje1oOr&sharer_shareinfo=b38f5f91b4efd8fb60303a4ef4774748&sharer_shareinfo_first=b38f5f91b4efd8fb60303a4ef4774748#rd)
* C++核心库，提供了整体的策略框架，在保证性能的同时，已经考虑了对多线程和多核处理的支持，在未来追求更高运算速度提供便利。C++核心库，可以单独剥离使用，自行构建自己的客户端工具。
* Python库（hikyuu），提供了对C++库的包装，同时集成了talib库（如TA_SMA，对应talib.SMA），可以与numpy、pandas数据结构进行互相转换，为使用其他成熟的python数据分析工具提供了便利。
* hikyuu.interactive 交互式探索工具，提供了K线、指标、系统信号等的基本绘图功能，用于对量化策略的探索和回测。
* **代码简洁，探索更便捷、自由** 同时支持面向对象和命令行编程范式。其中，命令行在进行策略探索时，代码简洁、探索更便捷、自由。
* **安全、自由、隐私，搭建自己的专属云量化平台** 结合 Python + Jupyter 的强大能力与云服务器，可以搭建自己专属的云量化平台。将Jupyter部署在云服务器上，随时随地的访问自己的云平台，即刻实现自己新的想法，如下图所示通过手机访问自己的云平台。结合Python强大成熟的数据分析、人工智能工具（如 numpy、scipy、pandas、TensorFlow)搭建更强大的人工智能平台。
* **数据存储方式可扩展** 目前支持本地HDF5格式、MySQL存储。默认使用HDF5，数据文件体积小、速度更快、备份更便利。截止至2017年4月21日，沪市日线数据文件149M、深市日线数据文件184M、5分钟线数据各不到2G。
# 想要更多了解Hikyuu？请使用以下方式联系：
## **加入知识星球**
更多示例与程序化交易的分享（您的加入将视为对项目的捐赠）。**作者只保证对知识星球用户有问必答，其他渠道视情况。(公众号文章末尾可能不定期发放优惠券)**
## 关注公众号：
## 加入微信群（请注明“加入hikyuu”）：
## QQ交流群：114910869, 或扫码加入
## 项目依赖说明
Hikyuu直接依赖以下开源项目（由以下项目间接依赖的项目未列出），感谢所有开源作者的贡献：
| 名称 | 项目地址 | License |
| ------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| xmake | [https://github.com/xmake-io/xmake](https://github.com/xmake-io/xmake) | Apache 2.0 |
| hdf5 | [https://github.com/HDFGroup/hdf5](https://github.com/HDFGroup/hdf5) | [hdf5 license](https://github.com/HDFGroup/hdf5?tab=License-1-ov-file#License-1-ov-file) |
| mysql(client) | [https://github.com/mysql/mysql-server]() | [mysql license](https://github.com/mysql/mysql-server?tab=License-1-ov-file#readme) |
| fmt | [https://github.com/fmtlib/fmt](https://github.com/fmtlib/fmt) | [fmt license](https://github.com/fmtlib/fmt?tab=License-1-ov-file#readme) |
| spdlog | [https://github.com/gabime/spdlog](https://github.com/gabime/spdlog) | MIT |
| sqlite | [https://www.sqlite.org/](https://www.sqlite.org/) | [sqlite license](https://www.sqlite.org/copyright.html) |
| flatbuffers | [https://github.com/google/flatbuffers](https://github.com/google/flatbuffers) | Apache 2.0 |
| nng | [https://github.com/nanomsg/nng](https://github.com/nanomsg/nng) | MIT |
| nlohmann_json | [https://github.com/nlohmann/json](https://github.com/nlohmann/json) | MIT |
| boost | [https://www.boost.org/](https://www.boost.org/) | [Boost Software License](https://www.boost.org/users/license.html) |
| python | [https://www.python.org/](https://www.python.org/) | [Python license](https://docs.python.org/3/license.html) |
| pybind11 | [https://github.com/pybind/pybind11](https://github.com/pybind/pybind11) | [pybind11 license](https://github.com/pybind/pybind11?tab=License-1-ov-file#readme) |
| gzip-hpp | [https://github.com/mapbox/gzip-hpp](https://github.com/mapbox/gzip-hpp) | BSD-2-Clause license |
| doctest | [https://github.com/doctest/doctest](https://github.com/doctest/doctest) | MIT |
Raw data
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"keywords": "quant, trade, System Trading, backtester, \u91cf\u5316, \u7a0b\u5e8f\u5316\u4ea4\u6613, \u91cf\u5316\u4ea4\u6613, \u7cfb\u7edf\u4ea4\u6613",
"author": "fasiondog",
"author_email": "fasiondog@sina.com",
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"description": "\n\n---\n\n     \n\nHikyuu Quant Framework\u662f\u4e00\u6b3e\u57fa\u4e8eC++/Python\u7684\u5f00\u6e90\u91cf\u5316\u4ea4\u6613\u7814\u7a76\u6846\u67b6\uff0c\u7528\u4e8e\u7b56\u7565\u5206\u6790\u53ca\u56de\u6d4b\uff08\u76ee\u524d\u4e3b\u8981\u7528\u4e8e\u56fd\u5185A\u80a1\u5e02\u573a\uff09\u3002\u5176\u6838\u5fc3\u601d\u60f3\u57fa\u4e8e\u5f53\u524d\u6210\u719f\u7684\u7cfb\u7edf\u5316\u4ea4\u6613\u65b9\u6cd5\uff0c\u5c06\u6574\u4e2a\u7cfb\u7edf\u5316\u4ea4\u6613\u62bd\u8c61\u4e3a\u7531\u5e02\u573a\u73af\u5883\u5224\u65ad\u7b56\u7565\u3001\u7cfb\u7edf\u6709\u6548\u6761\u4ef6\u3001\u4fe1\u53f7\u6307\u793a\u5668\u3001\u6b62\u635f/\u6b62\u76c8\u7b56\u7565\u3001\u8d44\u91d1\u7ba1\u7406\u7b56\u7565\u3001\u76c8\u5229\u76ee\u6807\u7b56\u7565\u3001\u79fb\u6ed1\u4ef7\u5dee\u7b97\u6cd5\u4e03\u5927\u7ec4\u4ef6\uff0c\u4f60\u53ef\u4ee5\u5206\u522b\u6784\u5efa\u8fd9\u4e9b\u7ec4\u4ef6\u7684\u7b56\u7565\u8d44\u4ea7\u5e93\uff0c\u5728\u5b9e\u9645\u7814\u7a76\u4e2d\u5bf9\u5b83\u4eec\u81ea\u7531\u7ec4\u5408\u6765\u89c2\u5bdf\u7cfb\u7edf\u7684\u6709\u6548\u6027\u3001\u7a33\u5b9a\u6027\u4ee5\u53ca\u5355\u4e00\u79cd\u7c7b\u7b56\u7565\u7684\u6548\u679c\u3002\n\n\ud83d\udc49 **\u9879\u76ee\u5730\u5740\uff1a**\n\n* [https://github.com/fasiondog/hikyuu](https://github.com/fasiondog/hikyuu)\n* [https://gitee.com/fasiondog/hikyuu](https://gitee.com/fasiondog/hikyuu)\n* [https://gitcode.com/hikyuu/hikyuu](https://gitcode.com/hikyuu/hikyuu)\n\n\ud83d\udc49 **\u9879\u76ee\u9996\u9875\uff1a**[https://hikyuu.org/](https://hikyuu.org/)\n\n\ud83d\udc49 **\u5e2e\u52a9\u6587\u6863\uff1a**[https://hikyuu.readthedocs.io/zh-cn/latest/index.html](https://hikyuu.readthedocs.io/zh-cn/latest/index.html)\n\n\ud83d\udc49 **\u5165\u95e8\u793a\u4f8b:** [https://nbviewer.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True](https://nbviewer.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True)\n\n\ud83d\udc49 **\u7b56\u7565\u90e8\u4ef6\u5e93\uff1a**[https://gitee.com/fasiondog/hikyuu_hub](https://gitee.com/fasiondog/hikyuu_hub)\n\n\ud83d\udc49 \u611f\u8c22\u7f51\u53cb\u63d0\u4f9b\u7684 Hikyuu Ubuntu\u865a\u62df\u673a\u73af\u5883, \u767e\u5ea6\u7f51\u76d8\u4e0b\u8f7d(\u63d0\u53d6\u7801: ht8j): [https://pan.baidu.com/s/1CAiUWDdgV0c0VhPpe4AgVw?pwd=ht8j](https://pan.baidu.com/s/1CAiUWDdgV0c0VhPpe4AgVw?pwd=ht8j)\n\n\u793a\u4f8b\uff1a\n\n```python\n #\u521b\u5efa\u6a21\u62df\u4ea4\u6613\u8d26\u6237\u8fdb\u884c\u56de\u6d4b\uff0c\u521d\u59cb\u8d44\u91d130\u4e07\n my_tm = crtTM(init_cash = 300000)\n\n #\u521b\u5efa\u4fe1\u53f7\u6307\u793a\u5668\uff08\u4ee55\u65e5EMA\u4e3a\u5feb\u7ebf\uff0c5\u65e5EMA\u81ea\u8eab\u768410\u65e5EMA\u4f5c\u4e3a\u6162\u7ebf\uff0c\u5feb\u7ebf\u5411\u4e0a\u7a7f\u8d8a\u6162\u7ebf\u65f6\u4e70\u5165\uff0c\u53cd\u4e4b\u5356\u51fa\uff09\n my_sg = SG_Flex(EMA(CLOSE(), n=5), slow_n=10)\n\n #\u56fa\u5b9a\u6bcf\u6b21\u4e70\u51651000\u80a1\n my_mm = MM_FixedCount(1000)\n\n #\u521b\u5efa\u4ea4\u6613\u7cfb\u7edf\u5e76\u8fd0\u884c\n sys = SYS_Simple(tm = my_tm, sg = my_sg, mm = my_mm)\n sys.run(sm['sz000001'], Query(-150))\n```\n\n\n\n\u5b8c\u6574\u793a\u4f8b\u53c2\u89c1\uff1a[https://nbviewer.jupyter.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True](https://nbviewer.jupyter.org/github/fasiondog/hikyuu/blob/master/hikyuu/examples/notebook/000-Index.ipynb?flush_cache=True)\n\n# \u4e3a\u4ec0\u4e48\u9009\u62e9 Hikyuu\uff1f\n\n* **\u7ec4\u5408\u7075\u6d3b\uff0c\u5206\u7c7b\u6784\u5efa\u7b56\u7565\u8d44\u4ea7\u5e93** Hikyuu\u5bf9\u7cfb\u7edf\u5316\u4ea4\u6613\u65b9\u6cd5\u8fdb\u884c\u4e86\u826f\u597d\u7684\u62bd\u8c61\uff0c\u5305\u542b\u4e86\u4e5d\u5927\u7b56\u7565\u7ec4\u4ef6\uff1a\u5e02\u573a\u73af\u5883\u5224\u65ad\u7b56\u7565\u3001\u7cfb\u7edf\u6709\u6548\u6761\u4ef6\u3001\u4fe1\u53f7\u6307\u793a\u5668\u3001\u6b62\u635f/\u6b62\u76c8\u7b56\u7565\u3001\u8d44\u91d1\u7ba1\u7406\u7b56\u7565\u3001\u76c8\u5229\u76ee\u6807\u7b56\u7565\u3001\u79fb\u6ed1\u4ef7\u5dee\u7b97\u6cd5\u3001\u4ea4\u6613\u5bf9\u8c61\u9009\u62e9\u7b56\u7565\u3001\u8d44\u91d1\u5206\u914d\u7b56\u7565\u3002\u53ef\u4ee5\u5728\u6b64\u57fa\u7840\u4e0a\u6784\u5efa\u81ea\u5df1\u7684\u7b56\u7565\u5e93\uff0c\u5e76\u8fdb\u884c\u7075\u6d3b\u7684\u7ec4\u5408\u548c\u6d4b\u8bd5\u3002\u5728\u8fdb\u884c\u7b56\u7565\u63a2\u7d22\u65f6\uff0c\u53ef\u4ee5\u66f4\u52a0\u4e13\u6ce8\u4e8e\u67d0\u4e00\u65b9\u9762\u7684\u7b56\u7565\u6027\u80fd\u4e0e\u5f71\u54cd\u3002\u5176\u4e3b\u8981\u529f\u80fd\u6a21\u5757\u5982\u4e0b\uff1a\n* **\u6027\u80fd\u4fdd\u969c\uff0c\u6253\u9020\u81ea\u5df1\u7684\u4e13\u5c5e\u5e94\u7528** \u76ee\u524d\u9879\u76ee\u5305\u542b\u4e863\u4e2a\u4e3b\u8981\u7ec4\u6210\u90e8\u5206\uff1a\u57fa\u4e8eC++\u7684\u6838\u5fc3\u5e93\u3001\u5bf9C++\u8fdb\u884c\u5305\u88c5\u7684Python\u5e93(hikyuu)\u3001\u57fa\u4e8ePython\u7684\u4ea4\u4e92\u5f0f\u5de5\u5177\u3002\n * AMD 7950x \u5b9e\u6d4b\uff1aA\u80a1\u5168\u5e02\u573a\uff081913\u4e07\u65e5K\u7ebf\uff09\u4ec5\u52a0\u8f7d\u5168\u90e8\u65e5\u7ebf\u8ba1\u7b97 20\u65e5 MA \u5e76\u6c42\u6700\u540e MA \u7d2f\u79ef\u548c\uff0c\u9996\u6b21\u6267\u884c\u542b\u6570\u636e\u52a0\u8f7d \u8017\u65f6 6\u79d2\uff0c\u6570\u636e\u52a0\u8f7d\u5b8c\u6bd5\u540e\u8ba1\u7b97\u8017\u65f6 166 \u6beb\u79d2\uff0c\u8be6\u89c1: [\u6027\u80fd\u5b9e\u6d4b](https://mp.weixin.qq.com/s?__biz=MzkwMzY1NzYxMA==&mid=2247483768&idx=1&sn=33e40aa9633857fa7b4c7ded51c95ae7&chksm=c093a09df7e4298b3f543121ba01334c0f8bf76e75c643afd6fc53aea1792ebb92de9a32c2be&mpshare=1&scene=23&srcid=05297ByHT6DEv6XAmyje1oOr&sharer_shareinfo=b38f5f91b4efd8fb60303a4ef4774748&sharer_shareinfo_first=b38f5f91b4efd8fb60303a4ef4774748#rd)\n * C++\u6838\u5fc3\u5e93\uff0c\u63d0\u4f9b\u4e86\u6574\u4f53\u7684\u7b56\u7565\u6846\u67b6\uff0c\u5728\u4fdd\u8bc1\u6027\u80fd\u7684\u540c\u65f6\uff0c\u5df2\u7ecf\u8003\u8651\u4e86\u5bf9\u591a\u7ebf\u7a0b\u548c\u591a\u6838\u5904\u7406\u7684\u652f\u6301\uff0c\u5728\u672a\u6765\u8ffd\u6c42\u66f4\u9ad8\u8fd0\u7b97\u901f\u5ea6\u63d0\u4f9b\u4fbf\u5229\u3002C++\u6838\u5fc3\u5e93\uff0c\u53ef\u4ee5\u5355\u72ec\u5265\u79bb\u4f7f\u7528\uff0c\u81ea\u884c\u6784\u5efa\u81ea\u5df1\u7684\u5ba2\u6237\u7aef\u5de5\u5177\u3002\n * 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\u7684\u5f3a\u5927\u80fd\u529b\u4e0e\u4e91\u670d\u52a1\u5668\uff0c\u53ef\u4ee5\u642d\u5efa\u81ea\u5df1\u4e13\u5c5e\u7684\u4e91\u91cf\u5316\u5e73\u53f0\u3002\u5c06Jupyter\u90e8\u7f72\u5728\u4e91\u670d\u52a1\u5668\u4e0a\uff0c\u968f\u65f6\u968f\u5730\u7684\u8bbf\u95ee\u81ea\u5df1\u7684\u4e91\u5e73\u53f0\uff0c\u5373\u523b\u5b9e\u73b0\u81ea\u5df1\u65b0\u7684\u60f3\u6cd5\uff0c\u5982\u4e0b\u56fe\u6240\u793a\u901a\u8fc7\u624b\u673a\u8bbf\u95ee\u81ea\u5df1\u7684\u4e91\u5e73\u53f0\u3002\u7ed3\u5408Python\u5f3a\u5927\u6210\u719f\u7684\u6570\u636e\u5206\u6790\u3001\u4eba\u5de5\u667a\u80fd\u5de5\u5177\uff08\u5982 numpy\u3001scipy\u3001pandas\u3001TensorFlow)\u642d\u5efa\u66f4\u5f3a\u5927\u7684\u4eba\u5de5\u667a\u80fd\u5e73\u53f0\u3002\n* **\u6570\u636e\u5b58\u50a8\u65b9\u5f0f\u53ef\u6269\u5c55** \u76ee\u524d\u652f\u6301\u672c\u5730HDF5\u683c\u5f0f\u3001MySQL\u5b58\u50a8\u3002\u9ed8\u8ba4\u4f7f\u7528HDF5\uff0c\u6570\u636e\u6587\u4ef6\u4f53\u79ef\u5c0f\u3001\u901f\u5ea6\u66f4\u5feb\u3001\u5907\u4efd\u66f4\u4fbf\u5229\u3002\u622a\u6b62\u81f32017\u5e744\u670821\u65e5\uff0c\u6caa\u5e02\u65e5\u7ebf\u6570\u636e\u6587\u4ef6149M\u3001\u6df1\u5e02\u65e5\u7ebf\u6570\u636e\u6587\u4ef6184M\u30015\u5206\u949f\u7ebf\u6570\u636e\u5404\u4e0d\u52302G\u3002\n\n![img](https://api.star-history.com/svg?repos=fasiondog/hikyuu&type=Date \"Star History Chart\")\n\n# \u60f3\u8981\u66f4\u591a\u4e86\u89e3Hikyuu\uff1f\u8bf7\u4f7f\u7528\u4ee5\u4e0b\u65b9\u5f0f\u8054\u7cfb\uff1a\n\n## 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\u9879\u76ee\u4f9d\u8d56\u8bf4\u660e\n\nHikyuu\u76f4\u63a5\u4f9d\u8d56\u4ee5\u4e0b\u5f00\u6e90\u9879\u76ee\uff08\u7531\u4ee5\u4e0b\u9879\u76ee\u95f4\u63a5\u4f9d\u8d56\u7684\u9879\u76ee\u672a\u5217\u51fa\uff09\uff0c\u611f\u8c22\u6240\u6709\u5f00\u6e90\u4f5c\u8005\u7684\u8d21\u732e\uff1a\n\n| \u540d\u79f0 | \u9879\u76ee\u5730\u5740 | License |\n| ------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |\n| xmake | [https://github.com/xmake-io/xmake](https://github.com/xmake-io/xmake) | Apache 2.0 |\n| hdf5 | [https://github.com/HDFGroup/hdf5](https://github.com/HDFGroup/hdf5) | [hdf5 license](https://github.com/HDFGroup/hdf5?tab=License-1-ov-file#License-1-ov-file) |\n| mysql(client) | [https://github.com/mysql/mysql-server]() | [mysql license](https://github.com/mysql/mysql-server?tab=License-1-ov-file#readme) |\n| fmt | [https://github.com/fmtlib/fmt](https://github.com/fmtlib/fmt) | [fmt license](https://github.com/fmtlib/fmt?tab=License-1-ov-file#readme) |\n| spdlog | [https://github.com/gabime/spdlog](https://github.com/gabime/spdlog) | MIT |\n| sqlite | [https://www.sqlite.org/](https://www.sqlite.org/) | [sqlite license](https://www.sqlite.org/copyright.html) |\n| flatbuffers | [https://github.com/google/flatbuffers](https://github.com/google/flatbuffers) | Apache 2.0 |\n| nng | [https://github.com/nanomsg/nng](https://github.com/nanomsg/nng) | MIT |\n| nlohmann_json | [https://github.com/nlohmann/json](https://github.com/nlohmann/json) | MIT |\n| boost | [https://www.boost.org/](https://www.boost.org/) | [Boost Software License](https://www.boost.org/users/license.html) |\n| python | [https://www.python.org/](https://www.python.org/) | [Python license](https://docs.python.org/3/license.html) |\n| pybind11 | [https://github.com/pybind/pybind11](https://github.com/pybind/pybind11) | [pybind11 license](https://github.com/pybind/pybind11?tab=License-1-ov-file#readme) |\n| gzip-hpp | [https://github.com/mapbox/gzip-hpp](https://github.com/mapbox/gzip-hpp) | BSD-2-Clause license |\n| doctest | [https://github.com/doctest/doctest](https://github.com/doctest/doctest) | MIT |\n",
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